Research Article
Machine Learning-Assisted in Silico Identification of Phytoconstituents as Potential NS5 RdRp Inhibitors for Dengue Therapeutics
Issue:
Volume 12, Issue 2, June 2026
Pages:
29-45
Received:
27 June 2026
Accepted:
20 July 2026
Published:
10 August 2026
DOI:
10.11648/j.jddmc.20261202.11
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Abstract: The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discovery (CADD) and machine learning (ML) framework to screen a library of 14 phytoconstituents against DENV-2 NS5 RdRp. Following molecular docking, the top-ranked compounds were evaluated for pharmacokinetic safety through cross-docking with CYP3A4, OATP1B1, and OATP1B3, complemented by ADMET prediction using SwissADME and ProTox-II. In parallel, ML-based quantitative structure-activity relationship (QSAR) models using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were developed using hybrid descriptors comprising Morgan fingerprints, physicochemical properties, experimental IC50 values, and docking scores derived from validated DENV RdRp inhibitors. Docking analysis identified Glycyrrhizin, Curcumin, Boswellic acid, Mangiferin, Azadirachtin, and Forskolin as promising inhibitors, exhibiting binding affinities comparable to or greater than those of remdesivir. Although several lead compounds demonstrated potential interactions with CYP3A4, their weak binding to OATP1B1 and OATP1B3 suggested a reduced risk of transporter-mediated toxicity and favorable hepatic safety, supported by ADMET predictions indicating favorable drug-like properties. Among the developed ML- based QSAR models, XGBoost outperformed RF in predicting nonlinear structure-activity relationships. Overall, the integrated molecular docking, pharmacokinetic profiling, and ML-based QSAR analyses identified Glycyrrhizin, Curcumin, Boswellic acid, and Azadirachtin as the most promising antiviral lead scaffolds and demonstrate the value of AI-driven drug discovery for accelerating antiviral lead identification. Further in vitro and in vivo studies are warranted to validate their therapeutic efficacy and safety.
Abstract: The escalating global burden of dengue virus (DENV) infection and the lack of specific antiviral therapies necessitate the development of effective therapeutics targeting the highly conserved non-structural protein 5 RNA-dependent RNA polymerase (NS5 RdRp). This study employed an integrated computer-aided drug discovery (CADD) and machine learning ...
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,
Jeevan Karthik Madavareddi
